The goal of this paper is to provide institutional investors, managers, and originators with a greater understanding of best practices for leveraging FinTECH to overcome inherent challenges with direct investing: liquidity, adverse selection, diversification, volatility, performance, scalability, access to best managers, and valuation.

Background

Institutional adoption of FinTECH platforms in the P2P sector has been robust. Prosper’s (leading P2P originator) lending origination grew by 347% in 2014 (YOY) issuing $1 billion worth of loans in just 6 months surpassing the $2 billion mark for the first time in the fall of 2014. Highlighting this accomplishment, it took Prosper 8 years to reach its first $1 billion in originations. Ron Suber, President – Prosper, terms this explosive growth “Escape Velocity” or escaping the gravitational pull of traditional lending practices and constraints (~banking industry).

And what accounted for Prosper’s escape velocity? Institutional capital! Hedge funds have been the primary catalyst for Prosper crossing the $5 billion mark in loan originations (October 2015). On the equity side, institutional capital is gaining access to early-stage technology and scaling capital primarily via investor syndicates (~online venture funds) participating both as “lead” syndicators (~Arena Ventures) and members of syndicates managed by esteemed investors.

“Every week, Who Wants to Be a Millionaire pitted group intelligence against individual intelligence, and every week, group intelligence won.”The Crowd! The phrase elicits a range of emotions from extremely pessimistic, “Either the Crowd is never wise, never reasonable and doomed to be extreme,” (Charles Mackay, publisher of Extraordinary Popular Delusions and the Madness of Crowds) to profoundly optimistic:

“This intelligence (~collective), or what I’ll call “the wisdom of crowds,” is at work in the world in many different guises. It’s the reason the Internet search engine Google can scan a billion Web pages and find the exact piece of information you were looking for. It’s the reason it’s so hard to make money betting on NFL games, and it helps explain why, for the past fifteen years, a few hundred amateur traders in the middle of Iowa have done a better job of predicting election results than Gallup polls have.”
– James Surowiecki in The Wisdom of Crowds

So which is it?!

Chasing the Expert

“Chasing the expert is a mistake, and a costly one at that. We should stop hunting and ask the crowd instead. Chances are, it knows.” – James Surowiecki in

Is the Crowd causing the death of experts? Not yet but the Crowd is having a measurable impact in select industries including finance as evidenced by the growing reluctance of investors to select experts over index funds (exchange traded funds – ETF’s) given their outperformance. “Between 1984 and 1999 almost 90% of mutual-fund managers underperformed the Wilshire 5000 Index, a relatively low bar. The numbers for bond-fund managers are similar: in the most recent five-year period, more than 95% of all managed bond funds underperformed the market.”

As illustrated above, it is not always wise to chase the experts and the body of evidence supporting this thesis is growing as the Crowd has increased access to data and tools to share knowledge with each other. With the advent of the Internet (Crowd’s access to data), advancements in social media (sharing amongst the Crowd), ability to tag, quantify, and personalize data (Crowd learning and collaborating), the Crowd can be heard and participate like never before. Being heard is one thing, contributing value is another.

There is little doubt the Crowd comprises some of the most intelligent humans on the planet. But it also includes those who are not. That is where basic statistics plays a major role:

“Then why do we cling so tightly to the idea that the right expert will save us? And why do we ignore the fact that simply averaging a group’s estimates will produce a very good result? Richard Larrick and Jack B. Soll suggest that the answer is that we have bad institutions about averaging. We assume averaging means dumbing down or compromising. When people are faced with the choice of picking one expert or picking pieces from a number of experts, they try to pick the best expert rather than simply average across the group.– James Surowiecki in

So if we can agree the Crowd provides valuable information/data collectively (oftentimes more accurate than the “experts”), the next step fueling this evolution entails the development and proliferation of tools encouraging the Crowd to contribute. One popular tool enticing the Crowd’s input resulting in “the collective” becoming more intelligent are Ratings & Reviews. Widely used in eCommerce platforms (~eBay) and growing in FinTECH (Peer-to-Peer, Equity Crowdfunding), Ratings & Reviews are pivotal for steering consumers/investors to those with the highest ranking from the Crowd and, as a byproduct, encouraging product/service providers to outperform. Ratings & Reviews implies “data in motion,” continually moving in a positive, negative, or neutral positions based on the Crowd’s sentiments over time.

“The Future of Peer-to-Peer Loans?
The Interview with Ron Suber, President of Prosper Marketplace”

Joseph Hogue, CrowdFunding Beat

Crowdfunding Beat – “How about the future of the industry? Where do you see peer loans in, let’s say, three years? Maybe even in five years?”

Ron Suber – “Continued integration of these peer-to-peer (P2P) platforms into the social network and the social community. If you look at Facebook today and go to the Prosper page on Facebook, you see thousands, tens of thousands of people telling their stories, sending in videos about how we helped them, and sending in photographs about the benefits of using Prosper. And you’ll start to see more and more of these technology firms embracing peer to peer (P2P) finance and payment. I think that will be a major driver in the next year or two ahead.”

Prominent FinTECH (Financial Technology) executives like Ron Suber, (President – Prosper), are realizing the benefits of marketing to borrowers/lenders on Facebook, Linkedin, and Twitter given social media’s “reach” (over a billion members on Facebook), and ease of sharing/building communities online including Friending” “Following” “Messaging” and “Liking.”

Below are the primary value propositions shared “virally” on social networks by leading FinTECH companies:

Let’s now look at some of the leading social media strategies facilitating referral behaviors.Some of these strategies are basic while other complex, regardless, used in combination and with frequency, social media tools empower “Escape Velocity” or exponential member growth …

What’s behind Peer-to-Peer’s meteoric rise or what some are now calling “Marketplace Lending”?

The emergence and adoption of FinTech. From Daniel Gorfine/Chris Brummer’s “FinTech Building a 21st Century Regulator’s Toolkit,” October, 2014 – “Online finance and investment platforms are increasingly challenging the providers of traditional financial services with efficient, low-cost, and user-friendly products and platforms”

Large Markets

Like the debt/lending industries, private equity is a huge market as measured by invested capital. Over $1 trillion in capital was invested in Reg D companies in 2013. These financial marketplaces, controlled by established intermediaries (venture capitalists, broker dealers, banks), are rife with inefficiencies/high fees for participating investors including management fees/carry (“2” and “20” – venture capital) and front-end fees/loads (10-15% on non-traded REIT investments – broker dealers). But for most accredited investors the primary issue still remains access to premium deal flow. Like P2P, Equity Crowdfunding allows all investors to participate, no preferential treatment or connections required.

Equity Crowdfunding portals are striving to reduce the risks associated with private equity investing by launching innovating financing platforms including Investor Syndicates. Given the risks associated with private equity investing (~illiquid, “hits” based business, high failure rate), investor syndicates were created to make it easier for investors to diversify across a number of promising companies/high-profile lead syndicators (esteemed investors with successful track records) via low investment thresholds (<$5,000 per investor per company).

If NO, what will prevent equity crowdfunding from matching P2P’s performance?

P2P Investor Onboarding Easier

The nuances of loaning money is well understood by investors given the prevalence of/familiarity with the banking industry and lending activity between friends and families. Debt is attractive to investors given it provides current income (yield) while protecting principal (return of capital at loan maturity). The financial markets have fed the market appetite for yield/protection of principal by providing robust analytics, reporting, ratings, and transparency contributing to the prevalence of debt (bonds) in most investor portfolios.

Given P2P loans are not classified as securities nor the organizations facilitating the lending activities identified as brokers or RIA’s, P2P platforms can more easily source deal flow (members fund loans directly from bank accounts). Ease of use and understanding/familiarity of debt has correlated to high rates of investor adoption similarly to those associated with eCommerce (~Amazon).

Investor onboarding via Equity Crowdfunding is more difficult. Investors are required to review and sign (electronic signatures available) term sheets/offering memorandums, and wire capital into escrow. As a result, many investors do not understand the process and/or given the number of additional steps (vs. P2P), do not complete the investment process.

P2P – Stimulates Institutional Adoption

Institutional capital is embracing marketplace lending platforms (about 66% of all Prosper loans in 2014 were sold to institutional investors). Peter Renton – Lend Academy, mentions in “Orchard Lands a $12 million investment from some of the industry’s biggest names,” October 21, 2014, the advent of the third lending model, “Historically, there have been two different business models in the lending industry: balance sheet and securitizations. What Lending Club (~Prosper) has proven is a third model is possible: the marketplace lending model.” Equity Crowdfunding is still pursuing institutional adoption.

P2P – Establishing Liquid Markets

Secondary markets stimulate institutional engagement by providing a marketplace for liquidity; an exchange where positions can be built or liquidated in real-time. Equity crowdfunding is still focused on building its primary market.

Foreseeing events before they happen. This power, largely associated with fortune tellers and Precogs in the movie Minority Report (predict crimes before they happen), is a reality. Companies are now actively collaborating with social media platforms (~Twitter) leveraging user data to predict future purchasing outcomes (predictive analytics) as a result of advances in Big Data/Crowdfunding.

In the National Bestseller “Big Data,” authors Vikto Mayer-Schonberger and Kenneth Cukier show how the retail chain Target “relies on predictions based on big-data correlations.” For example, knowing if customers are pregnant is important to retailers given it shapes shopping behaviors including going into new stores and developing brand loyalties. Target, using purchasing behavior data, determined which customers were most likely pregnant based on prediction scores (derived from a dozen products acting as proxies). These scores allowed Target to “estimate due dates within a narrow range so it could send relevant coupons for each stage of pregnancy.” Though Target’s practice ultimately was deemed controversial and a potential violation of privacy (teenager was solicited though she was pregnant), it illustrates the power of Big Data for predicating future purchasing outcomes.

Another example mentioned in “Big Data” involves “Likes.” Facebook utilizes “Likes” (members approving of user companies, news releases, comments, activities) to empower predictive analysis by associating member activities with future outcomes and customizes user news feeds matched to advertising. And Facebook has a lot of data to analyze given members willingly share information online; members click a “Like” button/leave a comment three billion times per day. Facebook tracks users’ “status updates” and “Likes” and determines the most suitable ads to display on its website to earn revenue.

So what is the intelligence behind Big Data/Crowdfunding enabling companies like Facebook and Target to predict future outcomes? That would be predictive analytic engines including Machine Intelligence, or applying math to large quantities of data in order to infer probabilities, and Data Mining (use of XML to tag words). Predictive analytics has risen in tandem with the social media revolution. When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, or Pandora magically creates your ideal playlist, these are examples of machine learning over a Big Data stream. (Source: “Machine Learning and Big Data Analytics: The Perfect Marriage,” Willem Waegeman)

Data is to the information society what fuel was to the industrial economy; the critical resource powering the innovations that people rely on. Let’s take a closer look at three of Silicon Valley’s predictive analytic crown jewels: Geo-Location, Social Graph, and Sentiment Analysis.

Geo-Location

A major technology embedded in high-flying tech/app companies like Uber and Wave, geo-location enables users to locate/schedule transportation and detect traffic jams (i.e., assessing the speed of phones traveling on highways). Geo-Location is inherently predictive allowing companies to serve ads based on user locations.

User locations in time and space leads to a wide range of apps delivering personalized content (contextual computing). “As the technologies and data underpinning contextification grow, there will be an increasing ability to actually predict user future context creating market pull rather than market push. In the future, being responsive to consumers will be a ticket to market failure. Rather, being predictive of consumer’s wants and needs will be expected.” (Source: Data Crush, Christopher Surdak)

“A company might know who my friends are in the city I am traveling to, what their availability is to meet with me while I’m in town, and the name of their favorite local restaurant. This information enables a marketing person to create a hyperlinked message.”

Case Study: Smart Glasses/Geo-Location

The Jamba Juice app downloaded to iGlass knows you are currently out shopping with your friend John (using facial recognition software on a video you posted on Facebook)

You are both near your local Jamba Juice shop (using location data from iGlasses)

Jamba Juice app knows to text an offer to the two of you for a two-for-one discount at that Jamba Juice if you both stop by in the next 15 minutes (Source: Data Crush, Christopher Surdak)

Social Graph

In 2013, Facebook had over one billion users interconnected via 100 billion friendships representing 10% of the world’s population. Social Graphs (global mapping of everybody and how they’re related) enable Facebook to assess user preferences when correlated with purchasing behavior including driving the company’s preeminent advertising platform. The more time users spend on Facebook, the more advertisers learn about users and the more valuable eyeballs become to them (average user spends over 400 minutes per month logged into the site).

Sentiment Analysis

Though limited to 140 characters, Tweets are rich in metadata including geo-location, user’s language, and #/names of those they follow. Hedge funds (Derwent Capital, MarketPsych) analyze sentiments in tweets to “signal investment” in the stock market.